Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	122,100
2	South Dakota	113,639
3	Wisconsin	90,382
4	Iowa	90,240
5	Utah	88,420
6	Nebraska	87,512
7	Tennessee	86,886
8	Rhode Island	83,020
9	Idaho	79,777
10	Kansas	79,068
11	Indiana	78,585
12	Wyoming	77,536
13	Illinois	77,503
14	Arkansas	77,365
15	Montana	77,079
16	Oklahoma	76,844
17	Arizona	76,439
18	Alabama	75,855
19	Nevada	75,278
20	Mississippi	74,613
21	Minnesota	74,579
22	New Mexico	69,816
23	Missouri	69,489
24	Louisiana	69,062
25	Alaska	65,404
26	Florida	63,574
27	Georgia	62,887
28	South Carolina	62,475
29	Texas	62,424
30	Kentucky	62,331
31	Ohio	61,722
32	California	61,597
33	Delaware	61,086
34	Colorado	59,473
35	Massachusetts	56,244
36	New Jersey	55,624
37	North Carolina	53,895
38	Michigan	53,674
39	Connecticut	53,325
40	New York	52,646
41	Pennsylvania	52,039
42	West Virginia	50,809
43	Maryland	47,221
44	Virginia	42,617
45	District of Columbia	42,173
46	Puerto Rico	35,001
47	New Hampshire	34,161
48	Washington	33,954
49	Oregon	27,916
50	Maine	19,038
51	Hawaii	15,550
52	Vermont	12,489

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	1,490
2	Oklahoma	1,106
3	West Virginia	1,064
4	California	1,063
5	Tennessee	1,014
6	Arkansas	920
7	South Carolina	916
8	New York	773
9	North Carolina	768
10	Georgia	742
11	Alabama	727
12	Nevada	724
13	Indiana	722
14	Kentucky	719
15	Utah	713
16	Mississippi	700
17	Delaware	694
18	Florida	653
19	Massachusetts	603
20	Ohio	601
21	New Hampshire	593
22	Kansas	560
23	New Mexico	555
24	Virginia	553
25	New Jersey	550
26	Pennsylvania	524
27	South Dakota	515
28	Alaska	503
29	Maryland	477
30	Texas	435
31	Illinois	427
32	Missouri	426
33	Louisiana	414
34	Connecticut	412
35	Colorado	400
36	Nebraska	371
37	District of Columbia	368
38	Maine	344
39	Wisconsin	332
40	Washington	327
41	Iowa	317
42	Minnesota	309
43	Oregon	301
44	Michigan	294
45	Wyoming	268
46	North Dakota	238
47	Idaho	220
48	Montana	212
49	Vermont	203
50	Puerto Rico	196
51	Hawaii	131
52	Rhode Island	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,162
2	New York	1,953
3	Massachusetts	1,829
4	North Dakota	1,728
5	Connecticut	1,710
6	South Dakota	1,710
7	Rhode Island	1,677
8	Mississippi	1,636
9	Louisiana	1,621
10	Illinois	1,445
11	Michigan	1,331
12	Pennsylvania	1,275
13	Indiana	1,258
14	Iowa	1,250
15	Arizona	1,244
16	Arkansas	1,242
17	New Mexico	1,217
18	District of Columbia	1,125
19	South Carolina	1,062
20	Nevada	1,037
21	Florida	1,023
22	Tennessee	1,019
23	Georgia	999
24	Alabama	993
25	Maryland	991
26	Kansas	988
27	Texas	984
28	Minnesota	973
29	Missouri	965
30	Delaware	955
31	Montana	909
32	Wisconsin	904
33	Nebraska	876
34	Colorado	867
35	Idaho	811
36	Ohio	776
37	West Virginia	767
38	Wyoming	756
39	California	674
40	Kentucky	674
41	North Carolina	663
42	Oklahoma	643
43	Virginia	600
44	New Hampshire	573
45	Puerto Rico	483
46	Washington	462
47	Utah	405
48	Oregon	358
49	Alaska	282
50	Maine	267
51	Vermont	224
52	Hawaii	202

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kansas	15
2	New Mexico	11
3	South Carolina	11
4	Indiana	10
5	Connecticut	9
6	Illinois	9
7	Massachusetts	9
8	Michigan	9
9	Mississippi	9
10	South Dakota	9
11	Arizona	8
12	Arkansas	8
13	New York	7
14	North Dakota	7
15	Pennsylvania	7
16	West Virginia	7
17	Colorado	6
18	Minnesota	6
19	Nevada	6
20	New Jersey	6
21	California	5
22	Iowa	5
23	Maryland	5
24	New Hampshire	5
25	Tennessee	5
26	Alaska	4
27	Florida	4
28	Kentucky	4
29	North Carolina	4
30	Oklahoma	4
31	Puerto Rico	4
32	Texas	4
33	Alabama	3
34	District of Columbia	3
35	Louisiana	3
36	Montana	3
37	Ohio	3
38	Utah	3
39	Virginia	3
40	Idaho	2
41	Maine	2
42	Missouri	2
43	Nebraska	2
44	Vermont	2
45	Delaware	1
46	Oregon	1
47	Wisconsin	1
48	Georgia	0
49	Hawaii	0
50	Rhode Island	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	273,882	1	99
Dewey	South Dakota	221,317	2	99
Norton	Kansas	220,854	3	99
Lincoln	Arkansas	218,673	4	99
Bon Homme	South Dakota	214,027	5	99
Davidson	Tennessee	102,810	284	90
Richland	South Carolina	65,247	1591	49
York	South Carolina	57,474	1984	36
Orange	California	53,784	2146	31
Pierce	Washington	31,554	2833	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Iron	Wisconsin	6,154	5	99
Davidson	Tennessee	830	1825	41
Richland	South Carolina	825	1835	41
York	South Carolina	662	2116	32
Orange	California	599	2222	29
Pierce	Washington	385	2599	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.